Sparsity-based method for ring artifact elimination in computed tomography.
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ABSTRACT:
SUBMITTER: Selim M
PROVIDER: S-EPMC9239489 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The metal artifact reduction (MAR) algorithm is used in most CBCT unit to reduce artifact from various dental materials. The performance of MAR program of a CBCT unit according to the dental material type under different imaging mode was evaluated as introducing automatic quantification of the amount of artifact reduced. Four customized phantoms with different dental prostheses (amalgam, gold, porcelain-fused-metal, zirconia) underwent CBCT scanning with and without the MAR option. The imaging was performed under varied scanning conditions; 0.2 and 0.3 mm3 voxel sizes; 70 and 100 kVp. The amount of artifacts reduced by each prosthesis and scanning mode automatically counted using canny edge detection in MATLAB, and statistical analysis was performed. The overall artifact reduction ratio was ranged from 17.3% to 55.4%. The artifact caused by the gold crown was most effectively reduced compared to the other prostheses (p?<?0.05, Welch's ANOVA analysis). MAR showed higher performance in smaller voxel size mode for all prostheses (p?<?0.05, independent t-test). Automatic quantification efficiently evaluated MAR performance in CBCT image. The impact of MAR was different according to the prostheses type and imaging mode, suggesting that thoughtful consideration is required when selecting the imaging mode of CBCT.
| S-EPMC7264136 | biostudies-literature
Project description:BackgroundObjective assessment of metal artifact strength and the effectiveness of metal artifact reduction algorithms in computed tomography requires a quantitative metric. Metrics described in the literature are typically employed to compare the artifact strength in images reconstructed from the same raw data, but their robustness against varying scan conditions and repeated scans over time as it occurs in periodic quality assurance has not been investigated.PurposeA new robust metric for quantifying metal artifacts in computed-tomography images is proposed and compared to other commonly used metrics.MethodsThe proposed artifact metric is based on the location parameter of the Gumbel distribution, described previously in the literature, but normalized to the location parameter in a background region-of-interest to obtain a noise-independent artifact metric. The metric was compared to three other quantitative metal artifact metrics (artifact-index, contrast-to-noise ratio, Gumbel-evaluation method) by evaluating metals artifacts in phantom scans and in clinical images. Robustness of the artifact metrics was evaluated using repeated scans with varying noise and against small variations in the selected regions-of-interest.ResultsThe proposed artifact metric was independent of the underlying image noise and could be reproduced more consistently under slight changes of the region-of-interest within the artifact than the other investigated methods. The coefficient-of-variation was 5.7% on average with varying regions-of-interest in phantom scans and 2.5% in patient scans compared to 9.2% in phantoms scans and 9.9% in patient scans for the next-best performing noise-independent metric. Setup reproducibility was better than 5% and was comparable to the other metrics. The new metric correlated linearly with the artifact strength. The contrast-to-noise ratio, although often used in artifact quantification, was found to be an inadequate metric due to its lack of robustness against minute changes in the position, size, and pixel values of the region-of-interest chosen for calculating the metric and because it showed no correlation with the artifact strength.ConclusionsA new metal artifact metric has been proposed that is robust under changing scan conditions and less sensitive to user-dependent choices of the region-of-interest than other metrics. The new metric is straightforward to calculate and simple to implement in software commonly used for evaluation of medical imaging systems.
| S-EPMC11302826 | biostudies-literature
Project description:BackgroundMulti-energy computed tomography (CT) provides multiple channel-wise reconstructed images, and they can be used for material identification and k-edge imaging. Nonetheless, the projection datasets are frequently corrupted by various noises (e.g., electronic, Poisson) in the acquisition process, resulting in lower signal-noise-ratio (SNR) measurements. Multi-energy CT images have local sparsity, nonlocal self-similarity in spatial dimension, and correlation in spectral dimension.MethodsIn this paper, we propose an image-spectral decomposition extended-learning assisted by sparsity (IDEAS) method to fully exploit these intrinsic priors for multi-energy CT image reconstruction. Particularly, a nonlocal low-rank Tucker decomposition (TD) is employed to utilize the correlation and nonlocal self-similarity priors. Moreover, considering the advantages of multi-task tensor dictionary learning (TDL) in sparse representation, an adaptive spatial dictionary and an adaptive spectral dictionary are trained during the iterative reconstruction process. Furthermore, a weighted total variation (TV) regularization term is employed to encourage local sparsity.ResultsNumerical simulation, physical phantom, and preclinical mouse experiments are performed to validate the proposed IDEAS algorithm. Specifically, in the simulation experiments, the proposed IDEAS reconstructed high-quality images that are very close to the references. For example, the root mean square error (RMSE) of IDEAS image in energy bin 1 is as low as 0.0672, while the RMSE of other methods are higher than 0.0843. Besides, the structural similarity (SSIM) of IDEAS reconstructed image in energy bin 1 is greater than 0.98. For material decomposition, the RMSE of IDEAS bone component is as low as 0.0152, and other methods are higher than 0.0199. In addition, the computational cost of IDEAS is as low as 98.8 s for one iteration, and the competing tensor decomposition method is higher than 327 s.ConclusionsTo further improve the quality of the reconstructed multi-energy CT images, multiple prior regularizations are introduced to the multi-energy CT reconstructed model, leading to an IDEAS method. Both qualitative and quantitative evaluation of our results confirm the outstanding performance of the proposed algorithm compared to the state-of-the-arts.
| S-EPMC9929415 | biostudies-literature
Project description:Dynamic cerebral perfusion computed tomography (DCPCT) has the ability to evaluate the hemodynamic information throughout the brain. However, due to multiple 3-D image volume acquisitions protocol, DCPCT scanning imposes high radiation dose on the patients with growing concerns. To address this issue, in this paper, based on the robust principal component analysis (RPCA, or equivalently the low-rank and sparsity decomposition) model and the DCPCT imaging procedure, we propose a new DCPCT image reconstruction algorithm to improve low-dose DCPCT and perfusion maps quality via using a powerful measure, called Kronecker-basis-representation tensor sparsity regularization, for measuring low-rankness extent of a tensor. For simplicity, the first proposed model is termed tensor-based RPCA (T-RPCA). Specifically, the T-RPCA model views the DCPCT sequential images as a mixture of low-rank, sparse, and noise components to describe the maximum temporal coherence of spatial structure among phases in a tensor framework intrinsically. Moreover, the low-rank component corresponds to the "background" part with spatial-temporal correlations, e.g., static anatomical contribution, which is stationary over time about structure, and the sparse component represents the time-varying component with spatial-temporal continuity, e.g., dynamic perfusion enhanced information, which is approximately sparse over time. Furthermore, an improved nonlocal patch-based T-RPCA (NL-T-RPCA) model which describes the 3-D block groups of the "background" in a tensor is also proposed. The NL-T-RPCA model utilizes the intrinsic characteristics underlying the DCPCT images, i.e., nonlocal self-similarity and global correlation. Two efficient algorithms using alternating direction method of multipliers are developed to solve the proposed T-RPCA and NL-T-RPCA models, respectively. Extensive experiments with a digital brain perfusion phantom, preclinical monkey data, and clinical patient data clearly demonstrate that the two proposed models can achieve more gains than the existing popular algorithms in terms of both quantitative and visual quality evaluations from low-dose acquisitions, especially as low as 20 mAs.
| S-EPMC5711606 | biostudies-literature
Project description:A major challenge of transcranial human brain photoacoustic computed tomography (PACT) is correcting for the acoustic aberration induced by the skull. Here, we present a modified universal back-projection (UBP) method, termed layered UBP (L-UBP), that can de-aberrate the transcranial PA signals by accommodating the skull heterogeneity into conventional UBP. In L-UBP, the acoustic medium is divided into multiple layers: the acoustic coupling fluid layer between the skull and detectors, the skull layer, and the brain tissue layer, which are assigned different acoustic properties. The transmission coefficients and wave conversion are considered at the fluid-skull and skull-tissue interfaces. Simulations of transcranial PACT using L-UBP were conducted to validate the method. Ex vivo experiments with a newly developed three-dimensional PACT system with 1-MHz center frequency demonstrated that L-UBP can substantially improve the image quality compared to conventional UBP.
| S-EPMC7586244 | biostudies-literature
Project description:In this paper, we make contact with the field of compressive sensing and present a development and generalization of tools and results for reconstructing irregularly sampled tomographic data. In particular, we focus on denoising Spectral-Domain Optical Coherence Tomography (SDOCT) volumetric data. We take advantage of customized scanning patterns, in which, a selected number of B-scans are imaged at higher signal-to-noise ratio (SNR). We learn a sparse representation dictionary for each of these high-SNR images, and utilize such dictionaries to denoise the low-SNR B-scans. We name this method multiscale sparsity based tomographic denoising (MSBTD). We show the qualitative and quantitative superiority of the MSBTD algorithm compared to popular denoising algorithms on images from normal and age-related macular degeneration eyes of a multi-center clinical trial. We have made the corresponding data set and software freely available online.
| S-EPMC3342198 | biostudies-other
Project description:Compressed sensing-based reconstruction algorithms have been proven to be more successful than analytical or iterative methods for sparse computed tomography (CT) imaging by narrowing down the solution set thanks to its ability to seek a sparser solution. Total variation (TV), one of the most popular sparsifiers, exploits spatial continuity of features by restricting variation between two neighboring pixels in each direction as using partial derivatives. When the number of projections is much fewer than the one in conventional CT, which results in much less sampling rate than the minimum required one, TV may not provide satisfactory results. In this study, a new regularizer is proposed which seeks for a sparser solution by reinforcing the gradient of TV and empowering the spatial continuity of features. The experiments are done by using both analitical phantom and real human CT images and the results are compared with conventional, four-directional, and directional TV algorithms by using contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR) and Structural Similarity Index (SSIM) metrics. Both quantitative and visual evaluations show that the proposed method is promising for sparse CT image reconstruction by reducing the background noise while preserving the features and edges.
| S-EPMC10782945 | biostudies-literature
Project description:Grating-based X-ray interferometry offers vast potential for imaging materials and tissues that are not easily visualised using conventional X-ray imaging. Tomographic reconstruction based on X-ray interferometric data provides not only access to the attenuation coefficient of an object, but also the refractive index and information about ultra-small-angle scattering. This improved functionality comes at the cost of longer measurement times because existing projection-based signal extraction algorithms require not only a single measurement per projection angle but several with precise grating movements in between. This obstacle hinders the adaptation of grating-based interferometry into a continuously rotating gantry. Several solutions to this problem have been proposed but all suffer from major drawbacks. We present results using an iterative reconstruction algorithm working directly on the interferograms. The suggested direct approach enables improved image quality, since interpolations and unnecessary assumptions about the object are circumvented. Our results demonstrate that it is possible to successfully reconstruct the linear attenuation coefficient, the refractive index and the linear diffusion coefficient, which is a measure related to ultra-small-angle scattering, using a single measurement per projection angle and without any grating movements. This is a milestone for future clinical implementation of grating-based phase-contrast and dark-field contrast X-ray computed tomography.
| S-EPMC5547164 | biostudies-other
Project description:BackgroundTo explore the feasibility and effectiveness of the metal artifact reduction software (MARs) reconstruction algorithm in reducing metal artifacts of knee prostheses and to explore the optimal monochromatic level of virtual monochromatic spectral (VMS) images for artifact reduction to provide high-quality images and reliable diagnosis in patients after total knee arthroplasty (TKA).MethodsA total of 31 patients underwent gemstone spectral computed tomography. VMS images with MARs and without MARs were obtained at different energy levels (80, 100, 120, and 140 keV). Two observers scored each group of images, and interobserver agreement was evaluated. Artificial indices (AIs), percentage500HU and structural similarity index measure (SSIM) values were calculated in the objective analysis to evaluate the image quality and impact of metal artifacts.ResultsThe consistency of the scores of the 2 observers was good (kappa value =0.78), and the score of the VMS images with MARs was higher than that of VMS images without MARs. AI values and percentage500HU of the MARs group were significantly lower than those of the without MARs group, while SSIM values were significantly higher. In the comparison of different keV images, the AI value decreased with the increase in keV in the range of 80-120 keV, but there was no significant difference between the 120 keV images and 140 keV images. In the group with MARs, the percentage500HU of 100-140 keV images was significantly lower than that of the 80 keV images, but there was no significant difference between 100, 120, and 140 keV images. In the group without MARs, the percentage500HU was significantly different among all keV groups.ConclusionsVMS images combined with the MARs algorithm can significantly reduce the metal artifacts of knee prostheses and improve image quality. At an energy level of 100-120 keV, a good metal artifact removal effect and soft tissue contrast can be achieved, and the best metal artifact removal effect can be achieved at 140 keV.
| S-EPMC9469117 | biostudies-literature
Project description:IntroductionTo date, no consensus has been reached on the optimal management of congenital lung abnormalities, and factors predicting postnatal outcome have not been identified. We developed an objective quantitative computed tomography (CT) scoring method, and assessed its value for clinical decision-making.MethodsVolumetric CT-scans of all patients born with a congenital lung abnormality between January 1999 and 2018 were assessed. Lung disease was quantified using the newly-developed congenital lung abnormality quantification (CLAQ) scoring method. In 20 equidistant axial slices, cells of a square grid were scored according to the abnormality within. The scored CT parameters were used to predict development of symptoms, and SD scores for spirometry and exercise tolerance (Bruce treadmill test) at 8 years of age.ResultsCT-scans of 124 patients with a median age of 5 months were scored. Clinical diagnoses included congenital pulmonary airway malformation (49%), bronchopulmonary sequestration (27%), congenital lobar overinflation (22%), and bronchogenic cyst (1%). Forty-four patients (35%) developed symptoms requiring surgery of whom 28 (22%) patients became symptomatic before a CT-scan was scheduled. Lesional hyperdensity was found as an important predictor of symptom development and decreased exercise tolerance. Using receiver operating characteristic analysis, an optimal cut-off value for developing symptoms was found at 18% total disease.ConclusionCT-quantification of congenital lung abnormalities using the CLAQ method is an objective and reproducible system to describe congenital lung abnormalities on chest CT. The risk for developing symptoms may increase when more than a single lung lobe is affected.
| S-EPMC7590128 | biostudies-literature